Human judgment for generative-AI labs.
Aligning and benchmarking generative models needs disciplined, calibrated human judgment at scale, not rushed clicks.
evaluate · rank · align
The problem
Alignment data is only as good as the evaluators behind it.
Improving an image or language model takes large volumes of careful human judgment across aesthetics, prompt faithfulness, text rendering and anatomy.
It has to be applied consistently by evaluators who don't rush and don't over-use “neither,” under rubrics that actually hold.
How io-ai helps.
Trained experts, an SOP-driven QA pipeline, and rejection-reason metadata on every task, held to over 98% accuracy.
Aesthetic & prompt-fidelity A/B
Quality and prompt-match judgments at scale.
Typography & anatomy QA
Letter-level checks and proportion / anatomy review.
Preference data / RLHF
Ranked preferences to align model behavior.
UI & screenshot annotation
Interface data with PII masked on every image.
Modalities & techniques
image evaluation
text
multimodal
preference ranking
Proven work.
A real generative-ai labs engagement, with a hard number.
Generative-AI Labs · Evaluation
4×
parallel evaluation frameworks, plus 50k UI screenshots labeled
Generative-AI image evaluation for a frontier model lab.
Anthropic
Read the case study
Let's talk
Tell us what you're building.
Send us your data challenge and we'll scope a pilot, usually within a couple of working days.